{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "fOeq8KIjs1mD"
      },
      "source": [
        "# Generating Graphs using Graph Neural Networks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 273,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "!pip install -q torch-scatter~=2.1.0 torch-sparse~=0.6.16 torch-cluster~=1.6.0 torch-spline-conv~=1.2.1 torch-geometric==2.2.0 -f https://data.pyg.org/whl/torch-{torch.__version__}.html\n",
        "!pip install -q deepchem==2.7.1\n",
        "\n",
        "torch.manual_seed(0)\n",
        "torch.cuda.manual_seed(0)\n",
        "torch.cuda.manual_seed_all(0)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Traditional techniques"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 274,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Fw4o-fWusWdw",
        "outputId": "7a021c30-764b-48bd-9bcb-25e818bc7439"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "G = nx.erdos_renyi_graph(10, 0.9, seed=0)\n",
        "pos = nx.circular_layout(G) \n",
        "nx.draw(G, pos=pos)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 275,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9U0yjv3onyRn",
        "outputId": "4c26dc94-ff42-497c-d237-75531c76a19a"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "G = nx.gnm_random_graph(3, 2, seed=3)\n",
        "pos = nx.circular_layout(G) \n",
        "nx.draw(G, pos=pos)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 276,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "clfQQfaUWwyp",
        "outputId": "dbf26ce3-8825-44ba-aa24-bb585ffd884c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "G = nx.watts_strogatz_graph(10, 4, 0.5, seed=0)\n",
        "pos = nx.circular_layout(G) \n",
        "nx.draw(G, pos=pos)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "GxVzdxEfsYj3"
      },
      "source": [
        "## Graph Variational Autoencoders (GVAE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 277,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIyyn_eumHuu",
        "outputId": "89e5ff8f-d194-4cba-b112-144008a63634"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch_geometric.transforms as T\n",
        "from torch_geometric.datasets import Planetoid\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "transform = T.Compose([\n",
        "    T.NormalizeFeatures(),\n",
        "    T.ToDevice(device),\n",
        "    T.RandomLinkSplit(num_val=0.05, num_test=0.1, is_undirected=True, split_labels=True, add_negative_train_samples=False),\n",
        "])\n",
        "\n",
        "dataset = Planetoid('.', name='Cora', transform=transform)\n",
        "\n",
        "train_data, val_data, test_data = dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 278,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2n6mXJ5mHsF",
        "outputId": "66867350-9efc-487b-b0bd-3090420b6546"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:   0 | Val AUC: 0.6806 | Val AP: 0.7086\n",
            "Epoch:  50 | Val AUC: 0.6828 | Val AP: 0.7075\n",
            "Epoch: 100 | Val AUC: 0.7499 | Val AP: 0.7558\n",
            "Epoch: 150 | Val AUC: 0.7611 | Val AP: 0.7741\n",
            "Epoch: 200 | Val AUC: 0.8365 | Val AP: 0.8329\n",
            "Epoch: 250 | Val AUC: 0.8853 | Val AP: 0.8915\n",
            "Epoch: 300 | Val AUC: 0.8869 | Val AP: 0.8925\n",
            "\n",
            "Test AUC: 0.8869 | Test AP: 0.8925\n"
          ]
        }
      ],
      "source": [
        "from torch_geometric.nn import GCNConv, VGAE\n",
        "\n",
        "class Encoder(torch.nn.Module):\n",
        "    def __init__(self, dim_in, dim_out):\n",
        "        super().__init__()\n",
        "        self.conv1 = GCNConv(dim_in, 2 * dim_out)\n",
        "        self.conv_mu = GCNConv(2 * dim_out, dim_out)\n",
        "        self.conv_logstd = GCNConv(2 * dim_out, dim_out)\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        x = self.conv1(x, edge_index).relu()\n",
        "        return self.conv_mu(x, edge_index), self.conv_logstd(x, edge_index)\n",
        "\n",
        "model = VGAE(Encoder(dataset.num_features, 16)).to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
        "\n",
        "def train():\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    z = model.encode(train_data.x, train_data.edge_index)\n",
        "    loss = model.recon_loss(z, train_data.pos_edge_label_index) + (1 / train_data.num_nodes) * model.kl_loss()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    return float(loss)\n",
        "\n",
        "@torch.no_grad()\n",
        "def test(data):\n",
        "    model.eval()\n",
        "    z = model.encode(data.x, data.edge_index)\n",
        "    return model.test(z, data.pos_edge_label_index, data.neg_edge_label_index)\n",
        "\n",
        "for epoch in range(301):\n",
        "    loss = train()\n",
        "    val_auc, val_ap = test(val_data)\n",
        "    if epoch % 50 == 0:\n",
        "        print(f'Epoch: {epoch:>3} | Val AUC: {val_auc:.4f} | Val AP: {val_ap:.4f}')\n",
        "\n",
        "val_auc, val_ap = test(val_data)\n",
        "print(f'\\nTest AUC: {val_auc:.4f} | Test AP: {val_ap:.4f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 279,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CgoF6g4cVBBi",
        "outputId": "d629d473-2b14-4093-eeea-204d4e6e4cd0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[1, 0, 0,  ..., 0, 1, 1],\n",
              "        [0, 1, 1,  ..., 0, 0, 0],\n",
              "        [0, 1, 1,  ..., 0, 1, 1],\n",
              "        ...,\n",
              "        [0, 0, 0,  ..., 0, 0, 0],\n",
              "        [1, 0, 1,  ..., 0, 1, 1],\n",
              "        [1, 0, 1,  ..., 0, 1, 1]], device='cuda:0')"
            ]
          },
          "execution_count": 279,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "z = model.encode(test_data.x, test_data.edge_index)\n",
        "adj = torch.where((z @ z.T) > 0.9, 1, 0)\n",
        "adj"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Vfve06mrde1z"
      },
      "source": [
        "## MolGAN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 301,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQOXyOIXoIqi",
        "outputId": "60ecbabd-7214-4914-c82f-3061dc9e3347"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tensorflow import one_hot\n",
        "\n",
        "import deepchem as dc\n",
        "from deepchem.models.optimizers import ExponentialDecay\n",
        "from deepchem.models import BasicMolGANModel as MolGAN\n",
        "from deepchem.feat.molecule_featurizers.molgan_featurizer import GraphMatrix\n",
        "\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem import rdmolfiles\n",
        "from rdkit.Chem import rdmolops\n",
        "from rdkit.Chem.Draw import IPythonConsole"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 302,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "n7uu4mgocrbP",
        "outputId": "e05f0900-7cb6-4e9f-8c26-b047dfa3810b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>smiles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CC(O)(P(=O)(O)O)P(=O)(O)O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CC(C)(C)OOC(C)(C)CCC(C)(C)OOC(C)(C)C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OC[C@H](O)[C@@H](O)[C@H](O)CO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CCCCCCCC(=O)[O-].CCCCCCCC(=O)[O-].[Zn+2]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CC(C)COC(=O)C(C)C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6259</th>\n",
              "      <td>CC1CCCCN1CCCOC(=O)c1ccc(OC2CCCCC2)cc1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6260</th>\n",
              "      <td>Cc1cc(CCCOc2c(C)cc(-c3noc(C(F)(F)F)n3)cc2C)on1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6261</th>\n",
              "      <td>O=C1OC(OC(=O)c2cccnc2Nc2cccc(C(F)(F)F)c2)c2ccc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6262</th>\n",
              "      <td>CC(=O)C1(C)CC2=C(CCCC2(C)C)CC1C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6263</th>\n",
              "      <td>CC(C)CCC[C@@H](C)[C@H]1CC(=O)C2=C3CC[C@H]4C[C@...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6264 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 smiles\n",
              "0                             CC(O)(P(=O)(O)O)P(=O)(O)O\n",
              "1                  CC(C)(C)OOC(C)(C)CCC(C)(C)OOC(C)(C)C\n",
              "2                         OC[C@H](O)[C@@H](O)[C@H](O)CO\n",
              "3              CCCCCCCC(=O)[O-].CCCCCCCC(=O)[O-].[Zn+2]\n",
              "4                                     CC(C)COC(=O)C(C)C\n",
              "...                                                 ...\n",
              "6259              CC1CCCCN1CCCOC(=O)c1ccc(OC2CCCCC2)cc1\n",
              "6260     Cc1cc(CCCOc2c(C)cc(-c3noc(C(F)(F)F)n3)cc2C)on1\n",
              "6261  O=C1OC(OC(=O)c2cccnc2Nc2cccc(C(F)(F)F)c2)c2ccc...\n",
              "6262                    CC(=O)C1(C)CC2=C(CCCC2(C)C)CC1C\n",
              "6263  CC(C)CCC[C@@H](C)[C@H]1CC(=O)C2=C3CC[C@H]4C[C@...\n",
              "\n",
              "[6264 rows x 1 columns]"
            ]
          },
          "execution_count": 302,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "_, datasets, _ = dc.molnet.load_tox21()\n",
        "df = pd.DataFrame(datasets[0].ids, columns=['smiles'])\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 303,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTC6G_hxc3pK",
        "outputId": "32cd9fdf-f4a1-4768-cae8-96f500f3d4b2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[23:33:22] WARNING: not removing hydrogen atom without neighbors\n",
            "Failed to featurize datapoint 0, CC(O)(P(=O)(O)O)P(=O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCCCNC(=S)NCCCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])[O-].O=[N+]([O-])[O-].[Ca+2]. Appending empty array\n",
            "Exception message: 20\n",
            "Failed to featurize datapoint 0, CC(C)CCCCCOC(=O)CCS. Appending empty array\n",
            "Exception message: 16\n",
            "[23:33:22] WARNING: not removing hydrogen atom without neighbors\n",
            "Failed to featurize datapoint 0, F[B-](F)(F)F.[H+]. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, [I-].[K+]. Appending empty array\n",
            "Exception message: 19\n",
            "Failed to featurize datapoint 0, C[Si](C)(C)N[Si](C)(C)C. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, C=CC(=O)OCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCC[Si](OC)(OC)OC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, ClCOCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Cu]I. Appending empty array\n",
            "Exception message: 29\n",
            "Failed to featurize datapoint 0, CO[Si](C)(C)OC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, BrCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCCCCCCCCOS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCC(Cl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)C[P+](C)(CC(C)C)CC(C)C. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, NC(=S)NNC(N)=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NC(=S)C(N)=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C[Hg]Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Hg+2]. Appending empty array\n",
            "Exception message: 80\n",
            "Failed to featurize datapoint 0, CNC(=O)/C=C(\\C)OP(=O)(OC)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, O=[Cr](=O)([O-])O[Cr](=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 24\n",
            "Failed to featurize datapoint 0, O=P(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)[C@@H](S)[C@H](S)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CO[Si](CCCS)(OC)OC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C[N+](C)=CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[Zr](Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)OS(C)(=O)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCN(CCC)C(=O)SCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(Cl)(Cl)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CCl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOP(=O)(CC)OCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CNC(=O)ON=CC(C)(C)SC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CSCCC=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)(C)C(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCC(CC)COC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CNC(=O)NC(O)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCOCCOCCSC#N. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, BrCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])[O-].[Na+].[Na+]. Appending empty array\n",
            "Exception message: 11\n",
            "Failed to featurize datapoint 0, [O-][Cl+3]([O-])([O-])[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCCSSCCO. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCCCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Ba+2]. Appending empty array\n",
            "Exception message: 56\n",
            "Failed to featurize datapoint 0, CCOC(=O)CP(=O)(OCC)OCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, N#CSCSC#N. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C[S+](C)CCC(N)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=CCN(CC=C)C(=O)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C[Si](C)(C)O[Si](C)(C)O[Si](C)(C)C. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CC[Sn](Br)(CC)CC. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCOP(=O)(OCC)OCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCOP(=S)(OCC)SCSCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCOP(=O)([O-])C(N)=O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, COC(=O)CCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCCCC(CC)COS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCCCCCC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)C(Cl)C(=O)OC. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NCCOS(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COP(=O)(OC)OC=C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(Cl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[Pd]Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC=CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])CCCS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [TlH2+]. Appending empty array\n",
            "Exception message: 81\n",
            "Failed to featurize datapoint 0, CC(C)OP(C)(=O)OC(C)C. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, COS(=O)(=O)OC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=P([O-])([O-])OC(CO)CO. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCCCCCCCP(=O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, C=C(Cl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OC(CCl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[Ba]Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)CP(=O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, ClC(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)CCSCCC(=O)OC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)O[Si](C)(OC(C)=O)OC(C)=O. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, ClC(Br)Br. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)(CCl)C(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC(Cl)=C(Cl)C(Cl)=C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)[O-].CC(=O)[O-].[Mg+2]. Appending empty array\n",
            "Exception message: 12\n",
            "Failed to featurize datapoint 0, ClC(Cl)(Cl)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Na]I. Appending empty array\n",
            "Exception message: 11\n",
            "Failed to featurize datapoint 0, BrCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCNC(=S)NCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)(C)S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cl/C=C/Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl/C=C\\Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCC(Cl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CP(=O)([O-])OCCC[Si](O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCOC(=O)C(=O)CCCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[Mo](=O)=O. Appending empty array\n",
            "Exception message: 42\n",
            "Failed to featurize datapoint 0, CCOP(=S)(S)OCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=[Cr]O[Cr]=O. Appending empty array\n",
            "Exception message: 24\n",
            "Failed to featurize datapoint 0, [Cr+3]. Appending empty array\n",
            "Exception message: 24\n",
            "Failed to featurize datapoint 0, N[C@@H](CSSC[C@H](N)C(=O)O)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCC(C)(S(=O)(=O)CC)S(=O)(=O)CC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCN(CC)C(=S)[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C[Sn](C)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCC(CC)C(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCC(O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [Fe+2]. Appending empty array\n",
            "Exception message: 26\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCCCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, N#C[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(N)=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCSC(=O)N(CCC)CCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClC(Cl)(Br)Br. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CSCCCN=C=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCC[Sn](Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 50\n",
            "Failed to featurize datapoint 0, CC(C)(S)C(=O)N[C@@H](CS)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=CCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, C=CCSSCCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=CCNC(N)=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cl[Nd](Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Co+2]. Appending empty array\n",
            "Exception message: 27\n",
            "Failed to featurize datapoint 0, CN(C)C(=S)[S-].CN(C)C(=S)[S-].[Cu+2]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)C(Br)C(=O)NC(N)=O. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])CO. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)C(Cl)C(=O)N(C)C. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=NN(CCCl)C(=O)NCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCO[Si](OCC)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CCCCCCCCSCCO. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C/C(Cl)=C(/Cl)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=CCSSCC=C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cl[Yb](Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NS(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [PbH2+2]. Appending empty array\n",
            "Exception message: 82\n",
            "Failed to featurize datapoint 0, OCC(S)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCNP(N)(N)=S. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, N[C@@H](CS)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)(O)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(CS)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCN(CCCl)CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Fe+3]. Appending empty array\n",
            "Exception message: 26\n",
            "Failed to featurize datapoint 0, CCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, ClC(Cl)Br. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OB(O)O. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, C=C(C)C(=O)OCCC[Si](Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Cu+2]. Appending empty array\n",
            "Exception message: 29\n",
            "Failed to featurize datapoint 0, ClCC(Cl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[In](Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=CO[AlH3](OC=O)OC=O. Appending empty array\n",
            "Exception message: 13\n",
            "Failed to featurize datapoint 0, CC(=O)O[AlH3](O)O. Appending empty array\n",
            "Exception message: 13\n",
            "Failed to featurize datapoint 0, [As]#[In]. Appending empty array\n",
            "Exception message: 33\n",
            "Failed to featurize datapoint 0, COS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCNC(=O)OCC#CI. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])[O-].[Li+].[Li+]. Appending empty array\n",
            "Exception message: 3\n",
            "Failed to featurize datapoint 0, CCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CS(=O)(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, S=C([S-])NCCNC(=S)[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C[N+]([O-])(CCCl)CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCC(C)S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(O)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, FC(F)(Cl)C(F)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, BrC(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, COP(=O)(NC(C)=O)SC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClP(Cl)(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=C[Si](C)(C)O[Si](C)(C)C=C. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, OCC(CBr)(CBr)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C(O)C(Cl)Br. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)C(Cl)(Cl)Br. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCCCCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CSSC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(Cl)(Cl)[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])C(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, NCCCNCCSP(=O)(O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CNC(=S)NN. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)C(F)(F)F. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCO[Si](C)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CCSCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCC[Sn](Cl)(Cl)CCCC. Appending empty array\n",
            "Exception message: 50\n",
            "Failed to featurize datapoint 0, CO[Si](OC)(OC)OC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, [C-]#N.[Cu+]. Appending empty array\n",
            "Exception message: 29\n",
            "Failed to featurize datapoint 0, CC(C)(C)C(=O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCC(CC)COP(=O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CC(S)C(=O)NCC(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [Cd+2]. Appending empty array\n",
            "Exception message: 48\n",
            "Failed to featurize datapoint 0, CC(CC(=O)Cl)CC(C)(C)C. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, C[N+](C)(C)CC(O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CNC(=S)[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [SnH2+2]. Appending empty array\n",
            "Exception message: 50\n",
            "Failed to featurize datapoint 0, COC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CNC(=O)O/N=C/C(C)(C)S(C)(=O)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=CC(Cl)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[Se]([O-])[O-]. Appending empty array\n",
            "Exception message: 34\n",
            "Failed to featurize datapoint 0, CCOC(=S)[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCN(CCCC)C(=S)[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cl[Au](Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCO[Si](CC)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])[O-].[Na+]. Appending empty array\n",
            "Exception message: 11\n",
            "Failed to featurize datapoint 0, N#CCCC(Br)(C#N)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCOP(=O)(OCC)SCCSCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, C=CC(=O)[O-].C=CC(=O)[O-].[Zn+2]. Appending empty array\n",
            "Exception message: 30\n",
            "Failed to featurize datapoint 0, CCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCCCCCI. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, CCOC(=O)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COP(OC)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, C[Se]CC[C@H](N)C(=O)O. Appending empty array\n",
            "Exception message: 34\n",
            "Failed to featurize datapoint 0, CN(C)C(=S)SC(=S)N(C)C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CN(C)C(=S)SSC(=S)N(C)C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, OC[P+](CO)(CO)CO. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCOS(C)(=O)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COP(=O)(O)OP(=O)(O)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, C[Si](C)(C)O[Si](C)(C)C. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, OCC(Br)=C(Br)CO. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, BrC/C=C/CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, N#CC(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])CCO. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=CCOCC(O)CS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COP(=O)(O)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, Cl[Dy](Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CS(=O)(=O)OCCCCOS(C)(=O)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCSSCCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NCCC(O)(P(=O)(O)O)P(=O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, N#CC(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=O)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, FB(F)F. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, OC(O)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC(Cl)=C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC(Cl)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClCC(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCCSCSCCO. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)[O-].[Na+]. Appending empty array\n",
            "Exception message: 11\n",
            "Failed to featurize datapoint 0, CC(=O)[O-].[K+]. Appending empty array\n",
            "Exception message: 19\n",
            "Failed to featurize datapoint 0, O=C(Cl)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CO[Si](CCCNCCN)(OC)OC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CCCCO[PH](=O)OCCCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCSC(=O)N(CC)CC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCBr. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(Br)C(=O)O. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, S=C([S-])NCCNC(=S)[S-].[Mn+2]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)O[AlH3](O)OC(C)=O. Appending empty array\n",
            "Exception message: 13\n",
            "Failed to featurize datapoint 0, OCCCCCCCCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, COS(=O)(=O)C(F)(F)F. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(O)C(Br)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, C=CC(=O)NC(C)(C)CS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCCCCCS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C([O-])CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)OB(OC(C)C)OC(C)C. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, CC(C)CCCCCOC(=O)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])SSS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, BrCC(Br)(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, BrC(Br)C(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CS(C)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(O)CCSSCCC(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CNC(=S)N(C)C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CSC(C)(C)/C=N\\O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, N#CCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClCBr. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C([O-])C(=O)[O-].[Ca+2]. Appending empty array\n",
            "Exception message: 20\n",
            "Failed to featurize datapoint 0, Br[Ca]Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CN(C)C(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C#CC(O)(/C=C\\Cl)CC. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, BrCCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=P([O-])(O)C(Cl)(Cl)P(=O)([O-])O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOP(OCC)OCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCCCCCC(C)(C)S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(O)CC(C(=O)O)S(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCSP(=O)(OCC)SCCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CC(CCl)OC(C)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCC(CO)(CBr)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, ClCCOCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCCSCCO. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, P#[In]. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, O=C(O)CC(Cl)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Mn+2]. Appending empty array\n",
            "Exception message: 25\n",
            "Failed to featurize datapoint 0, CCC(C)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, FC(F)OC(Cl)C(F)(F)F. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C([O-])[O-].[Sr+2]. Appending empty array\n",
            "Exception message: 38\n",
            "Failed to featurize datapoint 0, CNC(=S)NC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCOCCOCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)(C)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCCCSC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, N#CC(Br)(Br)C(N)=O. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCCCOP(=O)(O)OCCCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCCC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(Cl)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[Cd]. Appending empty array\n",
            "Exception message: 48\n",
            "Failed to featurize datapoint 0, S=C=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCOP(OCCCl)OCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOP(=S)(OCC)SCCSCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClC(Cl)(Cl)Br. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])CCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, OCC(O)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [Be+2]. Appending empty array\n",
            "Exception message: 4\n",
            "Failed to featurize datapoint 0, CC(C)(C)C(=O)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, F[B-](F)(F)F. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, O=S(=O)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=C(Cl)CSC(=S)N(CC)CC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C([O-])CC(S[Au])C(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CN(C)CCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=P(O)(O)CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=P(OCCCl)(OCCCl)OCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#CC(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=O)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(Cl)C(C)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC=C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)N[C@@H](CS)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [Zn+2]. Appending empty array\n",
            "Exception message: 30\n",
            "Failed to featurize datapoint 0, O=C(O)C(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C([O-])P(=O)([O-])[O-]. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, C=CS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCO[Si](CCCS)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])O.[Na+]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClSC(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N[Pt](N)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOC(=S)SSC(=S)OCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=C[Si](OC)(OC)OC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CCCCSCCCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COS(C)(=O)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(O)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NCCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O[C@@H]([C@H](O)[C@H](O)CBr)[C@H](O)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CO[Si](C)(OC)OC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CN(C)P(=O)(N(C)C)N(C)C. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CC(C)OC(=S)[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CNC(=O)ON=C(C)SC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCOC(=O)CC(=O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CNC(=O)CSP(=S)(OC)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, BrC(Br)C(Br)(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCOC(=O)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=[Se](O)O. Appending empty array\n",
            "Exception message: 34\n",
            "Failed to featurize datapoint 0, NC(N)=[Se]. Appending empty array\n",
            "Exception message: 34\n",
            "Failed to featurize datapoint 0, CCOS(=O)(=O)OCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=C(C)CS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCOC(=O)CCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CNCCS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCO[Si](CCCCl)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, FC(F)(F)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCSCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCC(Br)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, C[Si](C)(C)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C([O-])CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCSCCCCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(S)C(C)S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=[Se]=O. Appending empty array\n",
            "Exception message: 34\n",
            "Failed to featurize datapoint 0, ClC/C=C\\CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOC(=O)NC(O)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCC(Br)CBr. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, BrCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, IC(I)=C(I)I. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, O=P([O-])([O-])OP(=O)([O-])[O-]. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CNC(=O)ON=C(CSC)C(C)(C)C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCCCCCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, N#CCCSCCC#N. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(Cl)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)(Cl)[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCC(Cl)[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#CSCC(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCN(CC)CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCS(=O)(=O)C(C)(C)S(=O)(=O)CC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=CCN=C=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S([O-])CO. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCSC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, OCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=CCOCC(O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C/C(Cl)=C/CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCO[Si](CCC#N)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CCO[Si](CCCN)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, BrCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])OOS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])[O-].[K+]. Appending empty array\n",
            "Exception message: 19\n",
            "Failed to featurize datapoint 0, CS(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C[Si](C)(C)C. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, Cl[Zn]Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC/C=C/CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=S)NC(N)=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(Cl)(Cl)C(=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C[Sn](Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=CCS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=P([O-])(O)OP(=O)([O-])O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CN(CCCl)CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N=C(N)S(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COP(=O)(OC)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, C=C[Si](OCC)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CCCCN(CC)C(=O)SCCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cl[Ni]Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOP(=S)(Cl)OCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COP(=S)(Cl)OC. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[Sb](Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CNC(=O)ON=C(SC)C(=O)N(C)C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=[Bi]Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Br-].[Na+]. Appending empty array\n",
            "Exception message: 11\n",
            "Failed to featurize datapoint 0, CCCCCCCCOS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCP(CCCC)CCCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, N[C@@H](CSCCCO)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(C(Cl)(Cl)Cl)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CNC(=O)O/N=C/C(C)(C)S(C)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, II. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, CCCCC(CC)COC(=O)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCCCCCS(=O)(=O)Cl. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C([O-])C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CCl)NCO. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, SCCCCCCCCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(Cl)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)C(Br)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, ClC=C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClCC(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, FC(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COP(=O)(OC)C(O)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C[Si](C)=O. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, ClCC=CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOC(=O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])CC(S)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCO[Si](CC(C)C)(OCC)OCC. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])C(Br)(CO)CO. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, OCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, SCCSCCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCNC(C)(C)[PH](=O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCCCCCCCSCC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S(O)CO[Na]. Appending empty array\n",
            "Exception message: 11\n",
            "Failed to featurize datapoint 0, COP(C)(=O)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CNC(=O)O/N=C(\\C)C(C)S(C)(=O)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CN(C)C(=S)[S-].CN(C)C(=S)[S-].[Zn+2]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [Ca+2].[Cl-].[Cl-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCCCCI. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCCCI. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, CC(C)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, C[SiH](O[Si](C)(C)C)O[Si](C)(C)C. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, [SbH6+3]. Appending empty array\n",
            "Exception message: 51\n",
            "Failed to featurize datapoint 0, ClC(Cl)C(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)/C=C(\\C)OP(=O)(OC)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCS(=O)(=O)CCSP(=O)(OC)OC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(Cl)OCCOCCOC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=P(O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, NC(CSCC(=O)O)C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [Ni+2]. Appending empty array\n",
            "Exception message: 28\n",
            "Failed to featurize datapoint 0, C=CC(=O)NC(C)(C)CS(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])O[Cd]O[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 48\n",
            "Failed to featurize datapoint 0, CCOC(=O)C(Cl)C(C)=O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N[C@@H](COP(=O)(O)O)C(=O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, NNC(N)=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCS(=O)(=O)Cl. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NC(N)=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCOC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C[N+](C)(C)CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOP(=S)(OCC)SCCl. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC[Ge](Cl)(CC)CC. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COP(=O)(OC)OC(Br)C(Cl)(Cl)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCOP(=O)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCC[Sn](Cl)(CCCC)CCCC. Appending empty array\n",
            "Exception message: 50\n",
            "Failed to featurize datapoint 0, NCCCC(O)(P(=O)([O-])O)P(=O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, O=C(O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCC(C)SSC(C)CC. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=CC(Br)(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C(O)C(Br)(Br)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C(CS)OCCOC(=O)CS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=C(C)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCC(C)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, N#CCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C(O)CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)F. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(Cl)/C=C/C(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)CCSCCC(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, FC(F)(F)C(Cl)Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C(O)CCS. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CO[PH](=O)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CC(C)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCSC(=O)N(CC(C)C)CC(C)C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(CCl)C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#C[Au-]C#N. Appending empty array\n",
            "Exception message: 79\n",
            "Failed to featurize datapoint 0, ClC(Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCC(Br)(CC)C(=O)NC(N)=O. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCCSC(=O)Cl. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClC=CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NCCS(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCOP(O)OCC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, C[Si](C)(C)O[Si](O)(O)O. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, C=C(C)C(=O)OCCOP(=O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, FC(Cl)(Cl)C(F)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#C[Fe-2](C#N)(C#N)(C#N)(C#N)N=O. Appending empty array\n",
            "Exception message: 26\n",
            "Failed to featurize datapoint 0, CN(C)C(=S)[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCCCBr. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCCCCCCCCBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cl[Sn](Cl)(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(O)(P(=O)([O-])O)P(=O)([O-])O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, O=[Si]=O. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, CN=C=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCOCOCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)=CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C[As](C)(=O)O. Appending empty array\n",
            "Exception message: 33\n",
            "Failed to featurize datapoint 0, COP(N)(=O)SC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CNC(=O)CSP(=O)(OC)OC. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])CC(O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)CP(=S)([S-])CC(C)C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C([O-])C(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NNC(=S)NN. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCCCCCC(=O)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=O)CI. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, IC(I)I. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, FC(F)OC(F)(F)C(F)Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)(Cl)c1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1ccccc1S(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Oc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CNC(=O)Nc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)Nc1ccc(C)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCOP(=S)(CC)Sc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(O)c1cc(Cl)cc(Cl)c1O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cc([As](=O)(O)O)ccc1O. Appending empty array\n",
            "Exception message: 33\n",
            "Failed to featurize datapoint 0, C=CCOc1c(Br)cc(Br)cc1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc([As](=O)(O)O)cc1. Appending empty array\n",
            "Exception message: 33\n",
            "Failed to featurize datapoint 0, ClCc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)Cc1c(Cl)ccc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1cc(Cl)cc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClCc1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COc1cccc(Br)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CC(C)OC(=O)Nc1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COc1c(Br)cc(Br)c(C)c1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, NS(=O)(=O)c1ccc(C(=O)O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1cc(O)cc(C)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)c1ccc(CBr)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, S=C=NCc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCc1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C=Nc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=Cc1ccc(S(=O)(=O)[O-])cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1cc(C(F)(F)F)ccc1S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=Cc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=Cc1cccc(Br)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Clc1ccc(C(Cl)(Cl)Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CBr)c1ccccc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, COc1cc(Cl)c(OC)cc1N. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(C)c(N)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=S)Nc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1cc(Cl)cc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)c1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [S-]c1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1ccc(S(=O)(=O)O)c(N)c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CN(C)C(=O)Nc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(Oc1cc(Cl)ccc1Cl)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(Oc1ccc(Cl)cc1Cl)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)COc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cccc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cc(Cl)c(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1ccc(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1c(Cl)cc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cc(Cl)c(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1cc(Cl)cc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1c(Cl)ccc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC(Cl)c1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)OCc1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, BrCCc1ccccc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CC(C)NCC(O)c1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccccc1CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)c1ccc(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=O)Nc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(Cl)cc1[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1c(N)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(Br)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCCCNS(=O)(=O)c1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NC(=S)c1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)c1ccc(S(=O)(=O)[O-])cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1ccccc1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCNC(=O)/C=C/c1cccc(Br)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CN(C)c1ccc(SC#N)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1ccc(S(=O)(=O)O)cc1.O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, OCCN(CCO)c1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, BrCCOc1ccccc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, COc1c(Cl)ccc(Cl)c1C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Brc1ccc(Br)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Fc1c(F)c(F)c(Br)c(F)c1F. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCc1ccc(Br)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cc1ccccc1S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(CCCl)NCc1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])c1ccc(O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)Nc1ccc(S(N)(=O)=O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, FC(F)(F)c1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)Nc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1cc(Cl)c(N)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1c(Cl)cc([N+](=O)[O-])cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=C(C)C(=O)Nc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1cc(Cl)c(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccccc1CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc(CCl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1ccc([N+](=O)[O-])cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cccc(CCl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1cccc([N+](=O)[O-])c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1ccccc1[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(S(=O)(=O)[O-])cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)O[Hg]c1ccccc1. Appending empty array\n",
            "Exception message: 80\n",
            "Failed to featurize datapoint 0, NC(=O)c1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Fc1cccc(Cl)c1CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(Cl)ccc1/N=C/N(C)C. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C([O-])COc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CCl)Nc1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COc1ccc(OC)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(O)c(Cl)cc1C. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=O)c1ccc([N+](=O)[O-])cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COc1cc(CCN)c(OC)cc1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cc1ccc(S(=O)(=O)O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)c1ccc(S(=O)(=O)O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ICCc1ccccc1. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cccc(I)c1. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, Clc1cc(Cl)c(OCC#CI)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=S)NS(=O)(=O)c1ccc(N)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CON(C)C(=O)Nc1ccc(Br)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Nc1c(Cl)cc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1cc(Cl)ccc1O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1cc(Cl)c([N+](=O)[O-])cc1O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1cc(Cl)cc(S(=O)(=O)O)c1O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=Cc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#Cc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)c1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#CSCc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)c1cc(Cl)c(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)N(C(=O)CCl)c1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1c(Br)cc([N+](=O)[O-])cc1[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, N=C(N)NN=Cc1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=P(O)(O)c1ccccc1. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, Clc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1cc(Cl)cc([N+](=O)[O-])c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCC(=O)Nc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc(CBr)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cc1ccc(Br)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cc1ccc(S(=O)(=O)[O-])c(C)c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COc1ccc(Br)c(C)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Oc1c(Cl)c(Cl)cc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C=Cc1cccc(CCl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(O)c(Cl)c(C)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc([N+](=O)[O-])cc1S(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1ccc(S(N)(=O)=O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CON(C)C(=O)Nc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC[N+](C)(C)Cc1ccccc1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCC(=O)c1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)(C)c1ccc(S)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1cc(Cl)ccc1OCC(=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CS)Nc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC[Hg]Sc1ccccc1C(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(O)c1cc(Br)c(O)c(Br)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCOc1ccc(OCC)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1cc(S(=O)(=O)O)ccc1O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1cc(Cl)cc(C(=O)O)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])c1cc(O)ccc1O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COC(=O)c1cc(Cl)ccc1[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cccc(S(=O)(=O)[O-])c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1cc(Cl)ccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[PH](O)c1ccccc1. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, O=C(O)CNC(=O)c1ccccc1I. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, CCNC(=O)NCc1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CBr)c1ccc(O)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, FC(F)(F)c1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, FC(F)(F)c1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccccc1S(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NC(=O)Nc1ccc([As](=O)(O)O)cc1. Appending empty array\n",
            "Exception message: 33\n",
            "Failed to featurize datapoint 0, O=C(O)c1cc(I)cc(I)c1I. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, O=Cc1cc(O)ccc1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cc(I)c(O)c(I)c1. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, Clc1ccc(C(Cl)(Cl)Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(Cl)c(C)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1ccc(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [O-]c1cc(Cl)c(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COc1cc(Cl)ccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CSc1ccc(C=O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1ccc(C)c(S(=O)(=O)[O-])c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1ccc(Cl)c(O)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(O)ccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1c(Cl)cc(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COc1ccc(Cl)cc1C. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(Cl)ccc1N. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=O)NS(=O)(=O)c1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1c(Cl)ccc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(Cl)cc1N. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(N)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(N)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(Cl)cc1N. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(Cl)c(N)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Brc1c(Br)c(Br)c(Br)c(Br)c1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)c1ccccc1O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, N#Cc1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC(Cl)(Cl)c1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Sc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1cc(Cl)c(O)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1ccc([Hg]Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, SCc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)NS(=O)(=O)c1ccc(N)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Oc1cc(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1cc(C(=O)O)ccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#CCc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(O)c(C(C)C)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CO[Si](OC)(OC)c1ccccc1. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, Oc1cc(O)c(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1cccc(C(=O)Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)(N)Cc1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1ccccc1[Hg]Cl. Appending empty array\n",
            "Exception message: 80\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)c1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(O)c1cc(Cl)cc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1c(Cl)cc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1cc(Cl)cc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1cccc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1cc(Cl)ccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)(CCl)c1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc([As](=O)([O-])O)cc1. Appending empty array\n",
            "Exception message: 33\n",
            "Failed to featurize datapoint 0, CC(Oc1cccc(Cl)c1)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=Cc1ccc(F)c(Br)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C(O)c1ccccc1S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, OCC(O)COc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=P(O)(O)Oc1ccccc1. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, Clc1ccc(C(Cl)(Cl)Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1cc(Cl)c(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CCCCl)c1ccc(F)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=Cc1ccc(Cl)c([N+](=O)[O-])c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1c(Br)c(Br)c(Br)c(Br)c1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, NC(Cc1ccc(Cl)cc1)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, FC(F)(F)c1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, FC(F)(F)c1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc([As](=O)(O)O)cc1. Appending empty array\n",
            "Exception message: 33\n",
            "Failed to featurize datapoint 0, OCCSc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CSc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)c1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCOC(=O)c1ccc(I)cc1. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cc(C(F)(F)F)ccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COc1cc(Cl)c(OC)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Sc1ccccc1Cl. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCc1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Fc1cc(Br)ccc1CBr. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Oc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1c(Cl)cccc1[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(Cl)ccc1O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(S(=O)(=O)O)c(N)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(N)c(S(=O)(=O)O)c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1cc(N)c(S(=O)(=O)O)cc1Cl. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Clc1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Brc1ccc(Br)c(Br)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, N#Cc1cc(Br)c(O)c(Br)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, N#Cc1cc(I)c(O)c(I)c1. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)c1ccc(O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NC(=O)NS(=O)(=O)c1ccc(N)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Clc1cc(Cl)cc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1cccc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CCl)c1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1cc(Cl)c(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NCC(CC(=O)O)c1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(S(N)(=O)=O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1cccc(S(=O)(=O)O)c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(Cc1ccccc1)NCCCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Brc1ccccc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CC(C)(Oc1ccc(Cl)cc1)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1c(Br)cc(Br)cc1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cc1ccc(S(=O)(=O)[O-])cc1.[Na+]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, N=C(N)NS(=O)(=O)c1ccc(N)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1cc(Br)ccc1NC(=O)CCl. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Oc1ccc([Hg]Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)c1cc(Cl)cc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cccc(NC(=S)N(C)C)c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Oc1c(Cl)cc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)COc1cc(Cl)c(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])c1cc(S(=O)(=O)[O-])ccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COC(=O)c1cc(Cl)ccc1OC. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1cc(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1ccccc1OP(=O)(O)O. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, Cc1cccc(Br)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, BrCc1ccccc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCOC(=O)C(Br)c1ccccc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, NP(N)(=O)NC(=O)c1ccc(F)cc1. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[Hg]c1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccccc1S(N)(=O)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1cc(Cl)ccc1N=CN(C)C. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(Cl)c(N)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1ccccc1F. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Fc1ccc(Br)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C(O)Cc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1c(Cl)cc(Cl)cc1[N+](=O)[O-]. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(Cl)ccc1OC(C)C(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cc(Cl)ccc1OCC(=O)O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, S=C=NCCc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, S=C=Nc1ccccc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1ccc(S)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC(Cl)(Cl)c1ccc(C(Cl)(Cl)Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCNS(=O)(=O)c1ccc(C)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, N#CC(C#N)=NNc1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(C)c(S(=O)(=O)O)c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=Cc1ccccc1S(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1ccc(C(=O)Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(O)C(=O)O[Hg]c1ccccc1. Appending empty array\n",
            "Exception message: 80\n",
            "Failed to featurize datapoint 0, Cc1ccc(N)cc1S(=O)(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COC(=O)COc1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc(Cl)c([N+](=O)[O-])c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cccc(S(=O)(=O)[O-])c1C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C(CBr)OCc1ccccc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCCCCc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(S(=O)(=O)[O-])cc1C. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClP(Cl)c1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)(N)Cc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=Cc1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCC(=O)c1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1ccc(F)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OB(O)O[Hg]c1ccccc1. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1c(Cl)c(Cl)cc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])c1cc(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, BrCc1ccc(Br)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C(O)COc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1cc(Cl)ccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1cccc(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1ccc(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=Cc1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=Cc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Cl)c1ccc(F)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)(Cl)c1c(Cl)cccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NCc1ccc(S(N)(=O)=O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CO[Si](C)(OC)c1ccccc1. Appending empty array\n",
            "Exception message: 14\n",
            "Failed to featurize datapoint 0, Cc1ccc(S(=O)(=O)[N-]Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#CSc1ccc(N)c([N+](=O)[O-])c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)Nc1ccc(S(=O)(=O)Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc([N+](=O)[O-])cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cccc(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1c(Cl)c(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NS(=O)(=O)c1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CN(C)C(=O)Nc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc(S(=O)(=O)N=C=O)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)NC(C)Cc1ccc(I)cc1. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, N#Cc1c(Cl)c(Cl)c(Cl)c(C#N)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C=Nc1ccc(Cl)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=O)Nc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CCCl)NCCc1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(CCl)c1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)Nc1ccc(C(=O)CCl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CSc1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CS(=O)(=O)c1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Clc1ccccc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1nc(Cl)c(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1cccc(Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1ccccn1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NCCNC(=O)c1ccc(Cl)cn1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1nc(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClCc1ccccn1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClCc1cccnc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1nc(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1cccc(C(Cl)(Cl)Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1nc(C(Cl)(Cl)Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCc1cc(C(N)=S)ccn1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Clc1ccc(C(Cl)(Cl)Cl)cn1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, NC(=S)N/N=C/c1cccnc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1nc(C)c(Cl)c(O)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)c1nc(Cl)ccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1c(Cl)c(Cl)nc(C(=O)O)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, N#Cc1nc(Cl)c(Cl)c(Cl)c1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)COc1nc(Cl)c(Cl)cc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCCc1cc(C(N)=S)ccn1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Clc1cc(C(Cl)(Cl)Cl)cnc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1cc(Cl)c(Cl)nc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])c1cccc2ccccc12. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])c1ccc2ccccc2c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NC(=S)Nc1cccc2ccccc12. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)c1ccc2ccccc2c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Clc1cccc2ccccc12. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1ccc2ccccc2c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[C@H]1[C@H](Cl)[C@@H](Cl)[C@@H](Cl)[C@H](Cl)[C@H]1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)NC1CCCCC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, BrCC(Br)C1CCC(Br)C(Br)C1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, SC1CCCCC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC1CCC(C(C)CS)CC1S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S(=O)([O-])NC1CCCCC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClC1C(Cl)C(Cl)C(Cl)C(Cl)C1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[C@H]1[C@H](Cl)[C@@H](Cl)[C@H](Cl)[C@@H](Cl)[C@@H]1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[C@H]1[C@H](Cl)[C@@H](Cl)[C@H](Cl)[C@H](Cl)[C@H]1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCSC(=O)N(CC)C1CCCCC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C[n+]1cn(CCO)cc1.F[B-](F)(F)F. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, CCCn1cc[n+](C)c1.F[B-](F)(F)F. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, CCn1cc[n+](C)c1C.F[B-](F)(F)F. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, CCn1cc[n+](C)c1.N#C[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCn1cc[n+](C)c1.COS(=O)(=O)[O-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COP(=O)([O-])OC.Cn1cc[n+](C)c1. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, CCn1cc[n+](C)c1.F[B-](F)(F)F. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, Brc1ccc(Oc2ccccc2)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Brc1ccc(-c2ccccc2)cc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Clc1ccccc1-c1ccccc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Brc1cccc(-c2ccccc2)c1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Brc1ccccc1-c1ccccc1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Oc1ccc(-c2ccccc2)c(Cl)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc2c(Br)cc(Br)c(O)c2n1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Oc1c(Cl)cc(Cl)c2cccnc12. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ccc2c(Cl)cc(Cl)c(O)c2n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1c(Br)cc(Br)c2cccnc12. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cc1ccc2ccc(Cl)cc2n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1ccc(Cl)c2cccnc12. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1c(I)cc(Cl)c2cccnc12. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1c(I)cc(I)c2cccnc12. Appending empty array\n",
            "Exception message: 53\n",
            "Failed to featurize datapoint 0, [O-][n+]1ccccc1[S-]. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC[n+]1ccccc1.F[B-](F)(F)F. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, [O-][n+]1ccc(Cl)cc1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1nc(N)nc(Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCNc1nc(NCC)nc(SC)n1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Clc1nc(Cl)nc(Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCNc1nc(Cl)nc(NCC)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)Nc1nc(N)nc(Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCNc1nc(N)nc(Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CCNc1nc(Cl)nc(NC(C)C)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(C)SCc1ccco1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1c(S)cco1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CSSc1c(C)occ1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)SCc1ccco1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C[C@@H]1O[C@@H]1P(=O)([O-])[O-]. Appending empty array\n",
            "Exception message: 15\n",
            "Failed to featurize datapoint 0, BrCC1CO1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, ClCC1CO1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OC[C@H]1OC(S[Au])[C@H](O)[C@@H](O)[C@@H]1O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1nc2ccccc2s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1cc2c(cc1C)sc(N)n2. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1nc2c(Cl)cccc2s1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Nc1nc2ccc([N+](=O)[O-])cc2s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, N#CSCSc1nc2ccccc2s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Sc1nc2ccccc2s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, [S-]c1nc2ccccc2s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, c1ccc2scnc2c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1nc2ccccc2s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Brc1nc2ccccc2s1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cc1cccc2c1nc(N)s2. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COc1cccc2c1nc(N)s2. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, COc1ccc2nc(N)sc2c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCOc1ccc2nc(N)sc2c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1nc(Cl)cc(Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1cc(Cl)nc(Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1ccnc(Cl)n1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1ncc([N+](=O)[O-])n1CC(O)CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1c(CCCl)scn1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1cscn1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(C)Cc1nccs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1nc(CC(=O)O)cs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1nccs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)c1nccs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)Nc1ncc([N+](=O)[O-])s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1ncc([N+](=O)[O-])s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1c(CCO)scn1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCc1ncc(C(=O)O)s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)c1c(C)nc(C)s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cn1c(=O)c2c(nc(Br)[nH]2)n(C)c1=O. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=S(=O)(O)CCCN1CCOCC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, c1ccc2Sc3ccccc3Nc2c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1nc2ccc(Cl)cc2[nH]1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC1CCC(C(C)(C)S)C(=O)C1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C1CCCCC1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C1c2ccccc2C(=O)N1CCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, c1ccc(Sc2ccccc2)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC[N+]1(C)CCCC1.F[B-](F)(F)F. Appending empty array\n",
            "Exception message: 5\n",
            "Failed to featurize datapoint 0, COC(=O)c1nc(Cl)c(Cl)nc1N. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CSc1cnccn1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClCCN1CCCC1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C[C@H](CS)C(=O)N1CCC[C@H]1C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC1(C)C(=O)N(Cl)C(=O)N1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CC1(C)C(=O)N(Br)C(=O)N1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC1(C)C(=O)N(Br)C(=O)N1Br. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CC1(C)C(=O)N(Cl)C(=O)N1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, SC1CCCC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C1C(Cl)=C(Cl)C(=O)C(Cl)=C1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1ccccc1N1CCNCC1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1cccc(N2CCNCC2)c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C1OC(O)C(C(Cl)Cl)=C1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1c(Cl)c(Cl)c(Cl)s1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, COC(=O)c1c(S(N)(=O)=O)ccs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, c1cscc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1ccc(C=O)s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1c(Br)ccs1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, CCC(C)n1c(=O)[nH]c(C)c(Br)c1=O. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, Cc1c(Cl)c(=O)n(C(C)(C)C)c(=O)[nH]1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1nnc(Cl)c2ccccc12. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=c1[nH]c(=O)n(Cl)c(=O)n1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=c1n(Cl)c(=O)n(Cl)c(=O)n1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OCC1(CO)COC(C(Cl)(Cl)Cl)OC1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=[N+]([O-])C1(Br)COCOC1. Appending empty array\n",
            "Exception message: 35\n",
            "Failed to featurize datapoint 0, O=C1C(Cl)=C(Cl)C(=O)c2ccccc12. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, OC1(c2ccc(Cl)cc2)CCNCC1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1ccc2[nH]nnc2c1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Oc1ccc(Cl)c2c1CCC2. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=c1[nH]c2cc(Cl)ccc2o1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=c1[nH]c2ccc(Cl)cc2o1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Clc1nc2ccccc2nc1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, [Fe+2].c1c[cH-]cc1.c1c[cH-]cc1. Appending empty array\n",
            "Exception message: 26\n",
            "Failed to featurize datapoint 0, [Ni+2].c1c[cH-]cc1.c1c[cH-]cc1. Appending empty array\n",
            "Exception message: 28\n",
            "Failed to featurize datapoint 0, [Cr+2].c1c[cH-]cc1.c1c[cH-]cc1. Appending empty array\n",
            "Exception message: 24\n",
            "Failed to featurize datapoint 0, [Co+2].c1c[cH-]cc1.c1c[cH-]cc1. Appending empty array\n",
            "Exception message: 27\n",
            "Failed to featurize datapoint 0, NNc1nc(-c2ccccc2)cs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NNc1nc(-c2ccc(N)cc2)cs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Nc1nc(-c2ccccc2)cs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCCCCCCn1sccc1=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cn1sccc1=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cn1sc(Cl)cc1=O. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cc1c[nH]c(=S)[nH]c1=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cc1cc(=O)[nH]c(=S)[nH]1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=c1cc[nH]c(=S)[nH]1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCCc1cc(=O)[nH]c(=S)[nH]1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCSC(=O)N1CCCCCC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=C1CCC(=O)N1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, Cl[C@H]1OCCO[C@@H]1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, CC(=O)Nc1nnc(S(N)(=O)=O)s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Sc1nnc(S)s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=P1(NCCCl)OCCCN1CCCl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=P1(N(CCCl)CCCl)NCCCO1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=c1[nH]sc2ccccc12. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=c1[nH]sc2cc(Cl)ccc12. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC1=C(C(=O)O)N2C(=O)[C@@H](N)[C@H]2SC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CN1CCCN=C1/C=C/c1cccs1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Cn1cc[nH]c1=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CCOC(=O)n1ccn(C)c1=S. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, Oc1ccc(Cl)cc1C1CCCC1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C1Cc2cc(Cl)ccc2N1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(O)CCCCC1CCSS1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, NC(=O)CCCCC1CCSS1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)c1cc2ccccc2s1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, c1ccc2[nH]c(-c3cscn3)nc2c1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, c1ccc([C@H]2CN3CCSC3=N2)cc1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, ClC1=C(Cl)C(Cl)(Cl)C(Cl)=C1Cl. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, O=C(Nc1ccc(Cl)c(Cl)c1)C1CC1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C1CSCCS1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC1(O)CSC(C)(O)CS1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S1(=O)CCCC1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=S1(=O)CC(Cl)(Cl)C(Cl)(Cl)C1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC(=O)NC1CCSC1=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC1=Nc2ccc(Cl)cc2S(=O)(=O)N1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, C[C@H]1C[C@H](O)c2c(scc2)S1(=O)=O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, S=C1NC=NC2N=CNC12. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, S=c1[nH]c2ccccc2[nH]1. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, C=CCN1CCc2c(CC1)sc(N)n2. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, CC1(C)S[C@@H]2[C@H](N)C(=O)N2[C@H]1C(=O)O. Appending empty array\n",
            "Exception message: 16\n",
            "Failed to featurize datapoint 0, O=c1cc(O)c(Cl)c[nH]1. Appending empty array\n",
            "Exception message: 17\n",
            "Failed to featurize datapoint 0, ClC1=C(Cl)C1(Cl)Cl. Appending empty array\n",
            "Exception message: 17\n"
          ]
        }
      ],
      "source": [
        "max_atom = 15\n",
        "\n",
        "featurizer = dc.feat.MolGanFeaturizer(max_atom_count=max_atom)\n",
        "molecules = [x for x in df['smiles'].values if Chem.MolFromSmiles(x).GetNumAtoms() < max_atom]\n",
        "\n",
        "features = []\n",
        "for x in molecules:\n",
        "    mol = Chem.MolFromSmiles(x)\n",
        "    new_order = rdmolfiles.CanonicalRankAtoms(mol)\n",
        "    mol = rdmolops.RenumberAtoms(mol, new_order)\n",
        "    feature = featurizer.featurize(mol)\n",
        "    if feature.size != 0:\n",
        "        features.append(feature[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 304,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Remove invalid molecules\n",
        "features = [x for x in features if type(x) is GraphMatrix]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 305,
      "metadata": {
        "id": "eJHICx5lc3kO"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<NumpyDataset X.shape: (2107, 15, 15), y.shape: (2107, 15), w.shape: (2107, 1), task_names: [ 0  1  2 ... 12 13 14]>"
            ]
          },
          "execution_count": 305,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create MolGAN\n",
        "gan = MolGAN(learning_rate=ExponentialDecay(0.001, 0.9, 5000), vertices=max_atom)\n",
        "\n",
        "# Create dataset\n",
        "dataset = dc.data.NumpyDataset(X=[x.adjacency_matrix for x in features], y=[x.node_features for x in features])\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 306,
      "metadata": {
        "id": "RRfdbrz8dNI5"
      },
      "outputs": [],
      "source": [
        "def iterbatches(epochs):\n",
        "    for i in range(epochs):\n",
        "        for batch in dataset.iterbatches(batch_size=gan.batch_size, pad_batches=True):\n",
        "            adjacency_tensor = one_hot(batch[0], gan.edges)\n",
        "            node_tensor = one_hot(batch[1], gan.nodes)\n",
        "            yield {gan.data_inputs[0]: adjacency_tensor, gan.data_inputs[1]: node_tensor}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 307,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_p8_Pk0MdNGz",
        "outputId": "37a69aaa-e5f7-4b59-cced-c76721ae3258"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ending global_step 549: generator average loss -11.6004, discriminator average loss -14.2849\n",
            "TIMING: model fitting took 22.862 s\n"
          ]
        }
      ],
      "source": [
        "# Train model\n",
        "gan.fit_gan(iterbatches(25), generator_steps=0.2, checkpoint_interval=5000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 308,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecGB5jkwdNEk",
        "outputId": "ba8a72aa-c9ec-43f7-e6a5-47d759b28437"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating 1000 samples\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 F, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 16, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 22, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 23, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 24, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 21, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 F, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 24, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 C, 5, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 9, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 1 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 1 N, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 11 O, 5, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 2 C, 5, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 17, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 17, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 24, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 14, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 4 N, 10, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 F, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 17, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 N, 12, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 11, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 4 N, 8, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 20, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 1 N, 29, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 24, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 6 N, 9, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 1 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 O, 29, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 O, 4, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 25, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 7, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 9, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 C, 5, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 29, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 N, 10, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 4 N, 11, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 22, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 3 C, 5, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 N, 11, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 2 C, 5, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 1 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 24, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 17, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 N, 22, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 16, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 3 C, 7, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 6, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 3 C, 5, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 28, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 6 N, 24, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 26, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 9, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 24, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 20, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 N, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 19, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 10 F, 2, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 F, 27, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 O, 3, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 22, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 7, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 5 N, 18, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 7 C, 7, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 20, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 17, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 0 C, 25, is greater than permitted\n",
            "[23:33:49] Explicit valence for atom # 6 N, 7, is greater than permitted\n"
          ]
        }
      ],
      "source": [
        "# Generate 1000 samples\n",
        "generated_data = gan.predict_gan_generator(1000)\n",
        "generated_mols = featurizer.defeaturize(generated_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 309,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kXVSMwMHdRx8",
        "outputId": "4a486473-17a6-4ced-d4fa-de0c87b80d27"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "739 valid molecules (out of 1000 generated molecules)\n"
          ]
        }
      ],
      "source": [
        "# Check molecule validity (unstable so you might end up with 0 valid molecules)\n",
        "valid_mols = [x for x in generated_mols if x is not None]\n",
        "print (f'{len(valid_mols)} valid molecules (out of {len((generated_mols))} generated molecules)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 310,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQFcpTgSdRvg",
        "outputId": "6d6d865e-336d-40fe-a663-b248b0c54c79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "67 unique valid molecules (672 redundant molecules)\n"
          ]
        }
      ],
      "source": [
        "generated_smiles = [Chem.MolToSmiles(x) for x in valid_mols]\n",
        "generated_smiles_viz = [Chem.MolFromSmiles(x) for x in set(generated_smiles)]\n",
        "print(f'{len(generated_smiles_viz)} unique valid molecules ({len(generated_smiles)-len(generated_smiles_viz)} redundant molecules)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 311,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "wUuW8iNcdNBo",
        "outputId": "a3781374-4b90-436a-f66e-05c73cdf978e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "d:\\Programs\\Anaconda\\envs\\book\\lib\\site-packages\\rdkit\\Chem\\Draw\\IPythonConsole.py:258: UserWarning: Truncating the list of molecules to be displayed to 50. Change the maxMols value to display more.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x2000>"
            ]
          },
          "execution_count": 311,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Draw.MolsToGridImage(generated_smiles_viz, molsPerRow=5, subImgSize=(200, 200), returnPNG=False)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "book",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.15"
    },
    "vscode": {
      "interpreter": {
        "hash": "3556630122da5213751af4465d61fcf5a52cd22515d400aee51118aaa1721248"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
